在门户消息中自动发现患者的主要关注点,使用预训练语言模型的融合框架
Yang Ren1, Yuqi Wu2, Jungwei W Fan2
1Department of Computer Science and Engineering, University of South Carolina, Columbia, SC 29208, United States.
概括
这项研究引入了一个AI融合框架,使用语言模型准确识别患者门户消息中的患者主要关注点. 这种方法提高了医疗保健效率和以患者为中心的护理.
科学领域:
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
- 机器学习 机器学习
背景情况:
- 越来越多的患者门户信息 (PPM) 需要高效的分拣系统.
- 探索人工智能驱动的解决方案,以简化医疗保健工作流程.
- 在PPM中识别患者的主要关注点为改善护理提供了更深入的见解.
研究的目的:
- 开发和评估一个融合框架,以准确识别患者在PPM中的主要关注点.
- 为了比较各种语言模型和机器学习方法的性能.
主要方法:
- 提出了一个融合框架,利用预训练语言模型 (LMs) 与卷积神经网络.
- 用多类分类来检测主要关注点.
- 评估了传统的ML模型,基于BERT的LM,融合模型和组合模型.
主要成果:
- 基于BERT的模型表现优于传统的机器学习模型.
- 拟议的核聚变模型实现了最高的性能.
- 聚变模型的准确度为77.67%,F1得分为74.37% (宏观平均值).
结论:
- 多类分类对于在PPM中检测患者的主要关注点是有效的.
- 拟议的核聚变框架显著提高了这种检测的准确性和效率.
- 这种方法有助于管理大量的PPM,并确保及时的患者沟通.
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